Following an intensive discussion on LinkedIn about predictive analytics for the bioprocess industry, here a comment on strategies and tools that are used by the biotech industry:
The predictive analytical tools used for bioprocess design, analysis and control need to be differentiated:
First, there are information mining tools that are used in combination with multivariate and statistical data analysis. Relevant data from bioprocesses is mined, e.g. phase durations, growth characteristics, material attributes etc.) and evaluated (statistical workflows, MVDA tools) for process optimization, process trending, similarity analysis etc.
This is state of the art in the biotech industry and also a regulatory requirement for process validation as regards biopharma processes, see the 2011 process validation guideline. The Exputec inCyght bioprocess analysis software has integrated the full functionality necessary for this tasks (data management, information mining, multivariate/ statistical analysis/ reporting) and is used extensively for this purpose by biotech customers.
Second, there is mechanistic modelling, e.g. ODE models, applications in industry are e.g. microbial feeding profile optimizations, cell culture control algorithms for the control of glucose/ glutamine (especially relevant for biosimilars) or mechanistic soft-sensors.